{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2017:63GY7H44WS4RUSG2HC2V5GB27G","short_pith_number":"pith:63GY7H44","schema_version":"1.0","canonical_sha256":"f6cd8f9f9cb4b91a48da38b55e983af9be03ff92d03229c72870f5b92267f9f7","source":{"kind":"arxiv","id":"1702.00032","version":1},"attestation_state":"computed","paper":{"title":"Analyzing a stochastic process driven by Ornstein-Uhlenbeck noise","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cond-mat.stat-mech"],"primary_cat":"physics.data-an","authors_text":"B. Lehle, J. Peinke","submitted_at":"2017-01-31T19:38:33Z","abstract_excerpt":"A scalar Langevin-type process $X(t)$ that is driven by Ornstein-Uhlenbeck noise $\\eta(t)$ is non-Markovian. However, the joint dynamics of $X$ and $\\eta$ is described by a Markov process in two dimensions. But even though there exists a variety of techniques for the analysis of Markov processes, it is still a challenge to estimate the process parameters solely based on a given time series of $X$. Such a partially observed 2D-process could, e.g., be analyzed in a Bayesian framework using Markov chain Monte Carlo methods. Alternatively, an embedding strategy can be applied, where first the join"},"verification_status":{"content_addressed":true,"pith_receipt":true,"author_attested":false,"weak_author_claims":0,"strong_author_claims":0,"externally_anchored":false,"storage_verified":false,"citation_signatures":0,"replication_records":0,"graph_snapshot":true,"references_resolved":false,"formal_links_present":false},"canonical_record":{"source":{"id":"1702.00032","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"physics.data-an","submitted_at":"2017-01-31T19:38:33Z","cross_cats_sorted":["cond-mat.stat-mech"],"title_canon_sha256":"aad17734a2107870364187c023355cd0538689a61578638ae3874cce79d3d4d0","abstract_canon_sha256":"9c4353a5b69f7bc6db17e09caa082dbeaf93d7f7dda0d5b995d184f6a69fe47b"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T00:25:45.688728Z","signature_b64":"jfctR18Cnpcg8/k+wTyIeJTBL18hcfMaqPUImCd/vVKtRD5iUMnaCWNZqgqiZtbg+jSvKgRX3g6TJvSgHq20Bg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"f6cd8f9f9cb4b91a48da38b55e983af9be03ff92d03229c72870f5b92267f9f7","last_reissued_at":"2026-05-18T00:25:45.688106Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T00:25:45.688106Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Analyzing a stochastic process driven by Ornstein-Uhlenbeck noise","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cond-mat.stat-mech"],"primary_cat":"physics.data-an","authors_text":"B. Lehle, J. Peinke","submitted_at":"2017-01-31T19:38:33Z","abstract_excerpt":"A scalar Langevin-type process $X(t)$ that is driven by Ornstein-Uhlenbeck noise $\\eta(t)$ is non-Markovian. However, the joint dynamics of $X$ and $\\eta$ is described by a Markov process in two dimensions. But even though there exists a variety of techniques for the analysis of Markov processes, it is still a challenge to estimate the process parameters solely based on a given time series of $X$. Such a partially observed 2D-process could, e.g., be analyzed in a Bayesian framework using Markov chain Monte Carlo methods. Alternatively, an embedding strategy can be applied, where first the join"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1702.00032","kind":"arxiv","version":1},"verdict":{"id":null,"model_set":{},"created_at":null,"strongest_claim":"","one_line_summary":"","pipeline_version":null,"weakest_assumption":"","pith_extraction_headline":""},"references":{"count":0,"sample":[],"resolved_work":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57","internal_anchors":0},"formal_canon":{"evidence_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"author_claims":{"count":0,"strong_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"builder_version":"pith-number-builder-2026-05-17-v1"},"aliases":[{"alias_kind":"arxiv","alias_value":"1702.00032","created_at":"2026-05-18T00:25:45.688210+00:00"},{"alias_kind":"arxiv_version","alias_value":"1702.00032v1","created_at":"2026-05-18T00:25:45.688210+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1702.00032","created_at":"2026-05-18T00:25:45.688210+00:00"},{"alias_kind":"pith_short_12","alias_value":"63GY7H44WS4R","created_at":"2026-05-18T12:31:03.183658+00:00"},{"alias_kind":"pith_short_16","alias_value":"63GY7H44WS4RUSG2","created_at":"2026-05-18T12:31:03.183658+00:00"},{"alias_kind":"pith_short_8","alias_value":"63GY7H44","created_at":"2026-05-18T12:31:03.183658+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":0,"internal_anchor_count":0,"sample":[]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/63GY7H44WS4RUSG2HC2V5GB27G","json":"https://pith.science/pith/63GY7H44WS4RUSG2HC2V5GB27G.json","graph_json":"https://pith.science/api/pith-number/63GY7H44WS4RUSG2HC2V5GB27G/graph.json","events_json":"https://pith.science/api/pith-number/63GY7H44WS4RUSG2HC2V5GB27G/events.json","paper":"https://pith.science/paper/63GY7H44"},"agent_actions":{"view_html":"https://pith.science/pith/63GY7H44WS4RUSG2HC2V5GB27G","download_json":"https://pith.science/pith/63GY7H44WS4RUSG2HC2V5GB27G.json","view_paper":"https://pith.science/paper/63GY7H44","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1702.00032&json=true","fetch_graph":"https://pith.science/api/pith-number/63GY7H44WS4RUSG2HC2V5GB27G/graph.json","fetch_events":"https://pith.science/api/pith-number/63GY7H44WS4RUSG2HC2V5GB27G/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/63GY7H44WS4RUSG2HC2V5GB27G/action/timestamp_anchor","attest_storage":"https://pith.science/pith/63GY7H44WS4RUSG2HC2V5GB27G/action/storage_attestation","attest_author":"https://pith.science/pith/63GY7H44WS4RUSG2HC2V5GB27G/action/author_attestation","sign_citation":"https://pith.science/pith/63GY7H44WS4RUSG2HC2V5GB27G/action/citation_signature","submit_replication":"https://pith.science/pith/63GY7H44WS4RUSG2HC2V5GB27G/action/replication_record"}},"created_at":"2026-05-18T00:25:45.688210+00:00","updated_at":"2026-05-18T00:25:45.688210+00:00"}